Overview

Dataset statistics

Number of variables16
Number of observations20160
Missing cells144
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory162.2 B

Variable types

Numeric12
Categorical4

Alerts

Año is highly overall correlated with Mbps (Media de bajada) and 4 other fieldsHigh correlation
Mbps (Media de bajada) is highly overall correlated with Año and 3 other fieldsHigh correlation
+ 1 Mbps - 6 Mbps is highly overall correlated with + 6 Mbps - 10 Mbps and 1 other fieldsHigh correlation
+ 6 Mbps - 10 Mbps is highly overall correlated with + 1 Mbps - 6 Mbps and 1 other fieldsHigh correlation
+ 10 Mbps - 20 Mbps is highly overall correlated with TotalHigh correlation
+ 30 Mbps is highly overall correlated with Año and 4 other fieldsHigh correlation
OTROS is highly overall correlated with Año and 3 other fieldsHigh correlation
Total is highly overall correlated with + 1 Mbps - 6 Mbps and 4 other fieldsHigh correlation
Ingresos (miles de pesos) is highly overall correlated with Año and 4 other fieldsHigh correlation
Trimestre is highly overall correlated with PeriodoHigh correlation
Provincia_y is highly overall correlated with TotalHigh correlation
Periodo is highly overall correlated with Año and 2 other fieldsHigh correlation
Provincia_x is uniformly distributedUniform
Provincia_y is uniformly distributedUniform
Periodo is uniformly distributedUniform
+ 512 Kbps - 1 Mbps has 1200 (6.0%) zerosZeros
+ 6 Mbps - 10 Mbps has 912 (4.5%) zerosZeros
+ 10 Mbps - 20 Mbps has 1704 (8.5%) zerosZeros
+ 20 Mbps - 30 Mbps has 2496 (12.4%) zerosZeros
+ 30 Mbps has 2688 (13.3%) zerosZeros
OTROS has 10776 (53.5%) zerosZeros

Reproduction

Analysis started2023-04-23 19:02:22.067081
Analysis finished2023-04-23 19:03:13.972668
Duration51.91 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Año
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.8857
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size831.0 KiB
2023-04-23T16:03:14.427159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32020
95-th percentile2022
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.527302
Coefficient of variation (CV)0.0012524505
Kurtosis-1.2039785
Mean2017.8857
Median Absolute Deviation (MAD)2
Skewness0.022473737
Sum40680576
Variance6.3872556
MonotonicityDecreasing
2023-04-23T16:03:14.654155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 2304
11.4%
2020 2304
11.4%
2019 2304
11.4%
2018 2304
11.4%
2017 2304
11.4%
2016 2304
11.4%
2015 2304
11.4%
2014 2304
11.4%
2022 1728
8.6%
ValueCountFrequency (%)
2014 2304
11.4%
2015 2304
11.4%
2016 2304
11.4%
2017 2304
11.4%
2018 2304
11.4%
2019 2304
11.4%
2020 2304
11.4%
2021 2304
11.4%
2022 1728
8.6%
ValueCountFrequency (%)
2022 1728
8.6%
2021 2304
11.4%
2020 2304
11.4%
2019 2304
11.4%
2018 2304
11.4%
2017 2304
11.4%
2016 2304
11.4%
2015 2304
11.4%
2014 2304
11.4%

Trimestre
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size831.0 KiB
3
5184 
2
5184 
1
5184 
4
4608 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20160
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 5184
25.7%
2 5184
25.7%
1 5184
25.7%
4 4608
22.9%

Length

2023-04-23T16:03:14.864151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T16:03:15.328111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 5184
25.7%
2 5184
25.7%
1 5184
25.7%
4 4608
22.9%

Most occurring characters

ValueCountFrequency (%)
3 5184
25.7%
2 5184
25.7%
1 5184
25.7%
4 4608
22.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20160
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 5184
25.7%
2 5184
25.7%
1 5184
25.7%
4 4608
22.9%

Most occurring scripts

ValueCountFrequency (%)
Common 20160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 5184
25.7%
2 5184
25.7%
1 5184
25.7%
4 4608
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 5184
25.7%
2 5184
25.7%
1 5184
25.7%
4 4608
22.9%

Provincia_x
Categorical

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size831.0 KiB
Buenos Aires
 
840
Capital Federal
 
840
Tierra Del Fuego
 
840
Santiago Del Estero
 
840
Santa Fe
 
840
Other values (19)
15960 

Length

Max length19
Median length15
Mean length8.9166667
Min length5

Characters and Unicode

Total characters179760
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuenos Aires
2nd rowBuenos Aires
3rd rowBuenos Aires
4th rowBuenos Aires
5th rowBuenos Aires

Common Values

ValueCountFrequency (%)
Buenos Aires 840
 
4.2%
Capital Federal 840
 
4.2%
Tierra Del Fuego 840
 
4.2%
Santiago Del Estero 840
 
4.2%
Santa Fe 840
 
4.2%
Santa Cruz 840
 
4.2%
San Luis 840
 
4.2%
San Juan 840
 
4.2%
Salta 840
 
4.2%
Río Negro 840
 
4.2%
Other values (14) 11760
58.3%

Length

2023-04-23T16:03:15.545108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santa 1680
 
5.3%
la 1680
 
5.3%
del 1680
 
5.3%
san 1680
 
5.3%
entre 840
 
2.6%
rioja 840
 
2.6%
pampa 840
 
2.6%
jujuy 840
 
2.6%
formosa 840
 
2.6%
buenos 840
 
2.6%
Other values (24) 20160
63.2%

Most occurring characters

ValueCountFrequency (%)
a 23520
 
13.1%
e 14280
 
7.9%
o 12600
 
7.0%
11760
 
6.5%
n 10920
 
6.1%
u 10920
 
6.1%
r 10920
 
6.1%
t 8400
 
4.7%
s 7560
 
4.2%
i 7560
 
4.2%
Other values (30) 61320
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 136080
75.7%
Uppercase Letter 31920
 
17.8%
Space Separator 11760
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 23520
17.3%
e 14280
10.5%
o 12600
9.3%
n 10920
8.0%
u 10920
8.0%
r 10920
8.0%
t 8400
 
6.2%
s 7560
 
5.6%
i 7560
 
5.6%
l 4200
 
3.1%
Other values (15) 25200
18.5%
Uppercase Letter
ValueCountFrequency (%)
C 5880
18.4%
S 5040
15.8%
F 3360
10.5%
L 2520
7.9%
R 2520
7.9%
N 1680
 
5.3%
M 1680
 
5.3%
J 1680
 
5.3%
E 1680
 
5.3%
D 1680
 
5.3%
Other values (4) 4200
13.2%
Space Separator
ValueCountFrequency (%)
11760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 168000
93.5%
Common 11760
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 23520
14.0%
e 14280
 
8.5%
o 12600
 
7.5%
n 10920
 
6.5%
u 10920
 
6.5%
r 10920
 
6.5%
t 8400
 
5.0%
s 7560
 
4.5%
i 7560
 
4.5%
C 5880
 
3.5%
Other values (29) 55440
33.0%
Common
ValueCountFrequency (%)
11760
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 175560
97.7%
None 4200
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 23520
13.4%
e 14280
 
8.1%
o 12600
 
7.2%
11760
 
6.7%
n 10920
 
6.2%
u 10920
 
6.2%
r 10920
 
6.2%
t 8400
 
4.8%
s 7560
 
4.3%
i 7560
 
4.3%
Other values (26) 57120
32.5%
None
ValueCountFrequency (%)
í 1680
40.0%
ó 840
20.0%
é 840
20.0%
á 840
20.0%

Mbps (Media de bajada)
Real number (ℝ)

Distinct68
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.841667
Minimum3
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size831.0 KiB
2023-04-23T16:03:15.781996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median7
Q318
95-th percentile45
Maximum101
Range98
Interquartile range (IQR)14

Descriptive statistics

Standard deviation14.661128
Coefficient of variation (CV)1.0592025
Kurtosis5.2442687
Mean13.841667
Median Absolute Deviation (MAD)4
Skewness2.107705
Sum279048
Variance214.94869
MonotonicityNot monotonic
2023-04-23T16:03:16.050469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 2760
13.7%
4 2736
13.6%
6 2088
 
10.4%
5 1920
 
9.5%
7 960
 
4.8%
8 816
 
4.0%
9 600
 
3.0%
11 552
 
2.7%
12 552
 
2.7%
13 528
 
2.6%
Other values (58) 6648
33.0%
ValueCountFrequency (%)
3 2760
13.7%
4 2736
13.6%
5 1920
9.5%
6 2088
10.4%
7 960
 
4.8%
8 816
 
4.0%
9 600
 
3.0%
10 456
 
2.3%
11 552
 
2.7%
12 552
 
2.7%
ValueCountFrequency (%)
101 24
0.1%
94 24
0.1%
88 24
0.1%
83 24
0.1%
78 24
0.1%
73 24
0.1%
70 24
0.1%
68 24
0.1%
67 24
0.1%
66 24
0.1%

Provincia_y
Categorical

HIGH CORRELATION  UNIFORM 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size831.0 KiB
Buenos Aires
 
840
Capital Federal
 
840
Tierra Del Fuego
 
840
Santiago Del Estero
 
840
Santa Fe
 
840
Other values (19)
15960 

Length

Max length19
Median length15
Mean length8.9166667
Min length5

Characters and Unicode

Total characters179760
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuenos Aires
2nd rowCapital Federal
3rd rowCatamarca
4th rowChaco
5th rowChubut

Common Values

ValueCountFrequency (%)
Buenos Aires 840
 
4.2%
Capital Federal 840
 
4.2%
Tierra Del Fuego 840
 
4.2%
Santiago Del Estero 840
 
4.2%
Santa Fe 840
 
4.2%
Santa Cruz 840
 
4.2%
San Luis 840
 
4.2%
San Juan 840
 
4.2%
Salta 840
 
4.2%
Río Negro 840
 
4.2%
Other values (14) 11760
58.3%

Length

2023-04-23T16:03:16.302922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santa 1680
 
5.3%
la 1680
 
5.3%
del 1680
 
5.3%
san 1680
 
5.3%
entre 840
 
2.6%
rioja 840
 
2.6%
pampa 840
 
2.6%
jujuy 840
 
2.6%
formosa 840
 
2.6%
buenos 840
 
2.6%
Other values (24) 20160
63.2%

Most occurring characters

ValueCountFrequency (%)
a 23520
 
13.1%
e 14280
 
7.9%
o 12600
 
7.0%
11760
 
6.5%
n 10920
 
6.1%
u 10920
 
6.1%
r 10920
 
6.1%
t 8400
 
4.7%
s 7560
 
4.2%
i 7560
 
4.2%
Other values (30) 61320
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 136080
75.7%
Uppercase Letter 31920
 
17.8%
Space Separator 11760
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 23520
17.3%
e 14280
10.5%
o 12600
9.3%
n 10920
8.0%
u 10920
8.0%
r 10920
8.0%
t 8400
 
6.2%
s 7560
 
5.6%
i 7560
 
5.6%
l 4200
 
3.1%
Other values (15) 25200
18.5%
Uppercase Letter
ValueCountFrequency (%)
C 5880
18.4%
S 5040
15.8%
F 3360
10.5%
L 2520
7.9%
R 2520
7.9%
N 1680
 
5.3%
M 1680
 
5.3%
J 1680
 
5.3%
E 1680
 
5.3%
D 1680
 
5.3%
Other values (4) 4200
13.2%
Space Separator
ValueCountFrequency (%)
11760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 168000
93.5%
Common 11760
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 23520
14.0%
e 14280
 
8.5%
o 12600
 
7.5%
n 10920
 
6.5%
u 10920
 
6.5%
r 10920
 
6.5%
t 8400
 
5.0%
s 7560
 
4.5%
i 7560
 
4.5%
C 5880
 
3.5%
Other values (29) 55440
33.0%
Common
ValueCountFrequency (%)
11760
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 175560
97.7%
None 4200
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 23520
13.4%
e 14280
 
8.1%
o 12600
 
7.2%
11760
 
6.7%
n 10920
 
6.2%
u 10920
 
6.2%
r 10920
 
6.2%
t 8400
 
4.8%
s 7560
 
4.3%
i 7560
 
4.3%
Other values (26) 57120
32.5%
None
ValueCountFrequency (%)
í 1680
40.0%
ó 840
20.0%
é 840
20.0%
á 840
20.0%

HASTA 512 kbps
Real number (ℝ)

Distinct371
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.01
Minimum1.007
Maximum998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size831.0 KiB
2023-04-23T16:03:16.551923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.007
5-th percentile1.5348
Q110
median50
Q3134
95-th percentile556.35
Maximum998
Range996.993
Interquartile range (IQR)124

Descriptive statistics

Standard deviation187.47157
Coefficient of variation (CV)1.4877515
Kurtosis5.4033774
Mean126.01
Median Absolute Deviation (MAD)45.034
Skewness2.2984541
Sum2540361.6
Variance35145.589
MonotonicityNot monotonic
2023-04-23T16:03:16.942562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 480
 
2.4%
15 384
 
1.9%
18 384
 
1.9%
10 360
 
1.8%
16 360
 
1.8%
67 336
 
1.7%
71 288
 
1.4%
8 288
 
1.4%
26 264
 
1.3%
6 264
 
1.3%
Other values (361) 16752
83.1%
ValueCountFrequency (%)
1.007 24
 
0.1%
1.009 24
 
0.1%
1.01 72
 
0.4%
1.011 24
 
0.1%
1.053 24
 
0.1%
1.058 24
 
0.1%
1.063 24
 
0.1%
1.107 24
 
0.1%
1.11 192
1.0%
1.119 24
 
0.1%
ValueCountFrequency (%)
998 24
 
0.1%
991 24
 
0.1%
986 24
 
0.1%
973 24
 
0.1%
959 48
0.2%
958 72
0.4%
852 24
 
0.1%
847 24
 
0.1%
840 24
 
0.1%
791 24
 
0.1%

+ 512 Kbps - 1 Mbps
Real number (ℝ)

Distinct633
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.79039
Minimum0
Maximum999
Zeros1200
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size831.0 KiB
2023-04-23T16:03:17.923952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.3625
median8.788
Q384.3405
95-th percentile579.1
Maximum999
Range999
Interquartile range (IQR)80.978

Descriptive statistics

Standard deviation207.27778
Coefficient of variation (CV)2.0363197
Kurtosis6.6023363
Mean101.79039
Median Absolute Deviation (MAD)7.4075
Skewness2.6514864
Sum2052094.3
Variance42964.078
MonotonicityNot monotonic
2023-04-23T16:03:18.243624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1200
 
6.0%
1 288
 
1.4%
4 240
 
1.2%
285 192
 
1.0%
97 192
 
1.0%
109 168
 
0.8%
327 168
 
0.8%
909 144
 
0.7%
112 144
 
0.7%
40 120
 
0.6%
Other values (623) 17304
85.8%
ValueCountFrequency (%)
0 1200
6.0%
1 288
 
1.4%
1.027 24
 
0.1%
1.047 24
 
0.1%
1.058 24
 
0.1%
1.062 24
 
0.1%
1.077 24
 
0.1%
1.099 24
 
0.1%
1.123 24
 
0.1%
1.164 24
 
0.1%
ValueCountFrequency (%)
999 24
 
0.1%
995 24
 
0.1%
991 24
 
0.1%
987 48
 
0.2%
974 24
 
0.1%
940 48
 
0.2%
928 48
 
0.2%
909 144
0.7%
908 24
 
0.1%
900 24
 
0.1%

+ 1 Mbps - 6 Mbps
Real number (ℝ)

Distinct831
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150969.97
Minimum2842
Maximum2299705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size831.0 KiB
2023-04-23T16:03:18.478269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2842
5-th percentile11900.4
Q128539.25
median48834.5
Q386897.5
95-th percentile727863.25
Maximum2299705
Range2296863
Interquartile range (IQR)58358.25

Descriptive statistics

Standard deviation347955.17
Coefficient of variation (CV)2.3047973
Kurtosis20.971844
Mean150969.97
Median Absolute Deviation (MAD)25713
Skewness4.4515769
Sum3.0435546 × 109
Variance1.210728 × 1011
MonotonicityNot monotonic
2023-04-23T16:03:18.775272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35409 72
 
0.4%
14014 72
 
0.4%
58588 48
 
0.2%
22409 48
 
0.2%
28600 48
 
0.2%
30727 48
 
0.2%
40285 48
 
0.2%
47454 24
 
0.1%
404645 24
 
0.1%
48995 24
 
0.1%
Other values (821) 19704
97.7%
ValueCountFrequency (%)
2842 24
0.1%
3107 24
0.1%
3179 24
0.1%
3576 24
0.1%
3678 24
0.1%
4386 24
0.1%
5018 24
0.1%
5312 24
0.1%
5366 24
0.1%
6038 24
0.1%
ValueCountFrequency (%)
2299705 24
0.1%
2288772 24
0.1%
2281524 24
0.1%
2279875 24
0.1%
2267852 24
0.1%
2266948 24
0.1%
2253197 24
0.1%
2250898 24
0.1%
2250445 24
0.1%
2214760 24
0.1%

+ 6 Mbps - 10 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct757
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.417265
Minimum0
Maximum917
Zeros912
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size831.0 KiB
2023-04-23T16:03:19.043245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.0323
Q15.35725
median20.157
Q362.2255
95-th percentile311.42305
Maximum917
Range917
Interquartile range (IQR)56.86825

Descriptive statistics

Standard deviation140.24415
Coefficient of variation (CV)1.9637289
Kurtosis13.492374
Mean71.417265
Median Absolute Deviation (MAD)16.731
Skewness3.4980495
Sum1439772.1
Variance19668.422
MonotonicityNot monotonic
2023-04-23T16:03:19.418604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 912
 
4.5%
2 288
 
1.4%
11 96
 
0.5%
1 96
 
0.5%
655 72
 
0.4%
15 72
 
0.4%
26 48
 
0.2%
26.562 48
 
0.2%
1.915 48
 
0.2%
7.811 48
 
0.2%
Other values (747) 18432
91.4%
ValueCountFrequency (%)
0 912
4.5%
1 96
 
0.5%
1.034 24
 
0.1%
1.066 24
 
0.1%
1.133 24
 
0.1%
1.165 24
 
0.1%
1.227 24
 
0.1%
1.311 24
 
0.1%
1.314 24
 
0.1%
1.321 24
 
0.1%
ValueCountFrequency (%)
917 24
0.1%
902 24
0.1%
858 24
0.1%
855 24
0.1%
849 24
0.1%
792 24
0.1%
784 24
0.1%
779 24
0.1%
778 24
0.1%
775 24
0.1%

+ 10 Mbps - 20 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct726
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.988627
Minimum0
Maximum978
Zeros1704
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size831.0 KiB
2023-04-23T16:03:19.762327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.872
median14.9655
Q355.093
95-th percentile517.15
Maximum978
Range978
Interquartile range (IQR)50.221

Descriptive statistics

Standard deviation170.48926
Coefficient of variation (CV)2.1314187
Kurtosis10.112918
Mean79.988627
Median Absolute Deviation (MAD)12.402
Skewness3.1632077
Sum1612570.7
Variance29066.587
MonotonicityNot monotonic
2023-04-23T16:03:20.010597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1704
 
8.5%
1 120
 
0.6%
5 96
 
0.5%
10 72
 
0.4%
119 72
 
0.4%
100 72
 
0.4%
388 48
 
0.2%
22.91 48
 
0.2%
50 48
 
0.2%
559 48
 
0.2%
Other values (716) 17832
88.5%
ValueCountFrequency (%)
0 1704
8.5%
1 120
 
0.6%
1.061 24
 
0.1%
1.062 24
 
0.1%
1.076 24
 
0.1%
1.085 24
 
0.1%
1.162 24
 
0.1%
1.172 24
 
0.1%
1.202 24
 
0.1%
1.203 24
 
0.1%
ValueCountFrequency (%)
978 24
0.1%
966 24
0.1%
965 24
0.1%
958 24
0.1%
956 24
0.1%
920 24
0.1%
888 24
0.1%
886.678 24
0.1%
878 24
0.1%
832 24
0.1%

+ 20 Mbps - 30 Mbps
Real number (ℝ)

Distinct583
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.75816
Minimum0
Maximum997
Zeros2496
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size831.0 KiB
2023-04-23T16:03:20.281610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.019
median11.2515
Q364.28625
95-th percentile623
Maximum997
Range997
Interquartile range (IQR)62.26725

Descriptive statistics

Standard deviation208.56439
Coefficient of variation (CV)2.0699504
Kurtosis6.0472421
Mean100.75816
Median Absolute Deviation (MAD)11.2515
Skewness2.6012765
Sum2031284.5
Variance43499.103
MonotonicityNot monotonic
2023-04-23T16:03:20.582631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2496
 
12.4%
1 576
 
2.9%
5 432
 
2.1%
2 312
 
1.5%
3 192
 
1.0%
4 168
 
0.8%
22 120
 
0.6%
29 120
 
0.6%
13 96
 
0.5%
9 96
 
0.5%
Other values (573) 15552
77.1%
ValueCountFrequency (%)
0 2496
12.4%
1 576
 
2.9%
1.001 24
 
0.1%
1.032 24
 
0.1%
1.033 24
 
0.1%
1.068 24
 
0.1%
1.073 24
 
0.1%
1.084 24
 
0.1%
1.091 24
 
0.1%
1.135 24
 
0.1%
ValueCountFrequency (%)
997 24
0.1%
991 24
0.1%
979 24
0.1%
977 24
0.1%
969 24
0.1%
964 24
0.1%
961 24
0.1%
949.093 24
0.1%
941 24
0.1%
910 24
0.1%

+ 30 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct549
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79179.562
Minimum0
Maximum3618689
Zeros2688
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size831.0 KiB
2023-04-23T16:03:20.835634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median882.5
Q319660.75
95-th percentile322176.4
Maximum3618689
Range3618689
Interquartile range (IQR)19653.75

Descriptive statistics

Standard deviation342427.86
Coefficient of variation (CV)4.3247002
Kurtosis55.560759
Mean79179.562
Median Absolute Deviation (MAD)882.5
Skewness6.9778301
Sum1.59626 × 109
Variance1.1725684 × 1011
MonotonicityNot monotonic
2023-04-23T16:03:21.097616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2688
 
13.3%
2 936
 
4.6%
1 456
 
2.3%
3 360
 
1.8%
4 336
 
1.7%
10 312
 
1.5%
5 216
 
1.1%
22 192
 
1.0%
13 192
 
1.0%
9 168
 
0.8%
Other values (539) 14304
71.0%
ValueCountFrequency (%)
0 2688
13.3%
1 456
 
2.3%
2 936
 
4.6%
3 360
 
1.8%
4 336
 
1.7%
5 216
 
1.1%
6 24
 
0.1%
7 144
 
0.7%
8 120
 
0.6%
9 168
 
0.8%
ValueCountFrequency (%)
3618689 24
0.1%
3535757 24
0.1%
3381049 24
0.1%
3259793 24
0.1%
2482266 24
0.1%
2337604 24
0.1%
2246313 24
0.1%
2176242 24
0.1%
2085815 24
0.1%
1894466 24
0.1%

OTROS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct315
Distinct (%)1.6%
Missing144
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean32.937064
Minimum-1.945
Maximum923
Zeros10776
Zeros (%)53.5%
Negative48
Negative (%)0.2%
Memory size831.0 KiB
2023-04-23T16:03:21.386682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1.945
5-th percentile0
Q10
median0
Q37.515
95-th percentile120.464
Maximum923
Range924.945
Interquartile range (IQR)7.515

Descriptive statistics

Standard deviation128.8638
Coefficient of variation (CV)3.9124253
Kurtosis24.762292
Mean32.937064
Median Absolute Deviation (MAD)0
Skewness4.9840158
Sum659268.26
Variance16605.879
MonotonicityNot monotonic
2023-04-23T16:03:21.693156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10776
53.5%
2.151 144
 
0.7%
6.105 120
 
0.6%
1.035 120
 
0.6%
4.641 72
 
0.4%
2.68 72
 
0.4%
1.618 72
 
0.4%
3.719 72
 
0.4%
698 72
 
0.4%
792 72
 
0.4%
Other values (305) 8424
41.8%
(Missing) 144
 
0.7%
ValueCountFrequency (%)
-1.945 24
 
0.1%
-1 24
 
0.1%
0 10776
53.5%
1 24
 
0.1%
1.035 120
 
0.6%
1.123 24
 
0.1%
1.144 24
 
0.1%
1.246 24
 
0.1%
1.305 48
 
0.2%
1.313 24
 
0.1%
ValueCountFrequency (%)
923 24
 
0.1%
898 24
 
0.1%
895 24
 
0.1%
833 24
 
0.1%
803 24
 
0.1%
793 24
 
0.1%
792 72
0.4%
758 24
 
0.1%
735 48
0.2%
698 72
0.4%

Total
Real number (ℝ)

Distinct834
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean343988.81
Minimum12406
Maximum4721668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size831.0 KiB
2023-04-23T16:03:21.948812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12406
5-th percentile25701.75
Q152328.25
median104333
Q3177579.75
95-th percentile1417583.7
Maximum4721668
Range4709262
Interquartile range (IQR)125251.5

Descriptive statistics

Standard deviation736915.84
Coefficient of variation (CV)2.1422669
Kurtosis14.620783
Mean343988.81
Median Absolute Deviation (MAD)56426
Skewness3.7553583
Sum6.9348143 × 109
Variance5.4304496 × 1011
MonotonicityNot monotonic
2023-04-23T16:03:22.212889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14029 72
 
0.4%
35710 72
 
0.4%
68538 48
 
0.2%
33772 48
 
0.2%
177477 24
 
0.1%
27032 24
 
0.1%
88854 24
 
0.1%
80197 24
 
0.1%
637473 24
 
0.1%
88093 24
 
0.1%
Other values (824) 19776
98.1%
ValueCountFrequency (%)
12406 24
0.1%
12557 24
0.1%
12741 24
0.1%
13040 24
0.1%
13055 24
0.1%
13147 24
0.1%
13220 24
0.1%
13302 24
0.1%
13488 24
0.1%
13660 24
0.1%
ValueCountFrequency (%)
4721668 24
0.1%
4667183 24
0.1%
4555424 24
0.1%
4509157 24
0.1%
4251609 24
0.1%
4132351 24
0.1%
4060002 24
0.1%
4033261 24
0.1%
3971683 24
0.1%
3937277 24
0.1%

Ingresos (miles de pesos)
Real number (ℝ)

Distinct35
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20588449
Minimum2984054
Maximum67055930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size831.0 KiB
2023-04-23T16:03:22.529835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2984054
5-th percentile3270816
Q15936845
median14319467
Q332102476
95-th percentile60335724
Maximum67055930
Range64071876
Interquartile range (IQR)26165631

Descriptive statistics

Standard deviation17487275
Coefficient of variation (CV)0.84937312
Kurtosis0.15409193
Mean20588449
Median Absolute Deviation (MAD)9443082
Skewness1.0499122
Sum4.1506314 × 1011
Variance3.058048 × 1014
MonotonicityNot monotonic
2023-04-23T16:03:22.757840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
67055930 576
 
2.9%
5936845 576
 
2.9%
10065998 576
 
2.9%
9678647 576
 
2.9%
8701201 576
 
2.9%
7483980 576
 
2.9%
6912442 576
 
2.9%
6534241 576
 
2.9%
5376899 576
 
2.9%
13171459 576
 
2.9%
Other values (25) 14400
71.4%
ValueCountFrequency (%)
2984054 576
2.9%
3270816 576
2.9%
3478638 576
2.9%
3950441 576
2.9%
4701791 576
2.9%
4876385 576
2.9%
5153739 576
2.9%
5376899 576
2.9%
5936845 576
2.9%
6534241 576
2.9%
ValueCountFrequency (%)
67055930 576
2.9%
60335724 576
2.9%
55589997 576
2.9%
45467887 576
2.9%
42999944 576
2.9%
38239667 576
2.9%
36676371 576
2.9%
33539703 576
2.9%
32102476 576
2.9%
31997445 576
2.9%

Periodo
Categorical

HIGH CORRELATION  UNIFORM 

Distinct35
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size831.0 KiB
Jul-Sept 2022
 
576
Ene-Mar 2016
 
576
Jul-Sept 2017
 
576
Abr-Jun 2017
 
576
Ene-Mar 2017
 
576
Other values (30)
17280 

Length

Max length13
Median length12
Mean length12.257143
Min length12

Characters and Unicode

Total characters247104
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJul-Sept 2022
2nd rowJul-Sept 2022
3rd rowJul-Sept 2022
4th rowJul-Sept 2022
5th rowJul-Sept 2022

Common Values

ValueCountFrequency (%)
Jul-Sept 2022 576
 
2.9%
Ene-Mar 2016 576
 
2.9%
Jul-Sept 2017 576
 
2.9%
Abr-Jun 2017 576
 
2.9%
Ene-Mar 2017 576
 
2.9%
Oct-Dic 2016 576
 
2.9%
Jul-Sept 2016 576
 
2.9%
Abr-Jun 2016 576
 
2.9%
Oct-Dic 2015 576
 
2.9%
Ene-Mar 2018 576
 
2.9%
Other values (25) 14400
71.4%

Length

2023-04-23T16:03:22.997838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jul-sept 5184
12.9%
ene-mar 5184
12.9%
abr-jun 5184
12.9%
oct-dic 4608
11.4%
2016 2304
 
5.7%
2017 2304
 
5.7%
2015 2304
 
5.7%
2018 2304
 
5.7%
2014 2304
 
5.7%
2020 2304
 
5.7%
Other values (3) 6336
15.7%

Most occurring characters

ValueCountFrequency (%)
2 28224
 
11.4%
0 22464
 
9.1%
- 20160
 
8.2%
20160
 
8.2%
1 16128
 
6.5%
J 10368
 
4.2%
u 10368
 
4.2%
e 10368
 
4.2%
n 10368
 
4.2%
r 10368
 
4.2%
Other values (19) 88128
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 85824
34.7%
Decimal Number 80640
32.6%
Uppercase Letter 40320
16.3%
Dash Punctuation 20160
 
8.2%
Space Separator 20160
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 10368
12.1%
e 10368
12.1%
n 10368
12.1%
r 10368
12.1%
t 9792
11.4%
c 9216
10.7%
b 5184
6.0%
a 5184
6.0%
p 5184
6.0%
l 5184
6.0%
Decimal Number
ValueCountFrequency (%)
2 28224
35.0%
0 22464
27.9%
1 16128
20.0%
6 2304
 
2.9%
7 2304
 
2.9%
5 2304
 
2.9%
8 2304
 
2.9%
4 2304
 
2.9%
9 2304
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
J 10368
25.7%
A 5184
12.9%
M 5184
12.9%
E 5184
12.9%
S 5184
12.9%
O 4608
11.4%
D 4608
11.4%
Dash Punctuation
ValueCountFrequency (%)
- 20160
100.0%
Space Separator
ValueCountFrequency (%)
20160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 126144
51.0%
Common 120960
49.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 10368
 
8.2%
u 10368
 
8.2%
e 10368
 
8.2%
n 10368
 
8.2%
r 10368
 
8.2%
t 9792
 
7.8%
c 9216
 
7.3%
b 5184
 
4.1%
A 5184
 
4.1%
a 5184
 
4.1%
Other values (8) 39744
31.5%
Common
ValueCountFrequency (%)
2 28224
23.3%
0 22464
18.6%
- 20160
16.7%
20160
16.7%
1 16128
13.3%
6 2304
 
1.9%
7 2304
 
1.9%
5 2304
 
1.9%
8 2304
 
1.9%
4 2304
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 247104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 28224
 
11.4%
0 22464
 
9.1%
- 20160
 
8.2%
20160
 
8.2%
1 16128
 
6.5%
J 10368
 
4.2%
u 10368
 
4.2%
e 10368
 
4.2%
n 10368
 
4.2%
r 10368
 
4.2%
Other values (19) 88128
35.7%

Interactions

2023-04-23T16:03:08.895389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:26.612288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:30.999103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:35.669611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:39.784765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:42.694131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:46.076889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:49.840939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:52.732391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:58.442404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:01.714834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:05.296374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:09.151410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:27.148208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:31.538461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:36.164247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:40.016537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:42.914117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:46.335856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:50.157965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:53.184006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:58.691388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:01.948388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:05.525373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:09.404386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:27.421889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:31.964287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:36.549377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:40.274569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:43.119135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:46.649861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:50.381942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:53.681855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:58.910402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:02.268611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:05.769154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:09.646482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:27.753965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:32.236281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:36.853615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:40.475570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:43.314137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:46.921382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:50.599965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:54.073965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:59.139445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:02.660582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:06.129987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:10.229495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:28.051687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:32.562339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:37.200609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:40.680559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:43.629614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:47.169392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:50.810967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:54.401958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:59.358445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:02.916581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:06.345013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:10.488509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:28.288699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:32.849142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:37.459856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:40.882231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:44.027407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:47.463297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:51.066451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:54.847976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:59.776563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:03.149927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:06.607991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:10.731501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:28.528713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:33.155151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:37.810620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:41.088251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:44.276428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:47.975741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:51.305448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:56.181417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:00.101617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:03.356906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:06.870580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:11.058499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:28.792109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:33.609207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:38.245104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:41.467900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:44.538410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:48.181738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:51.542732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:56.912495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:00.409634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:03.569913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:07.199902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:11.319179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:29.033109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:33.942862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:38.572100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:41.669195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:44.840599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:48.466742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:51.776979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:57.226413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:00.698620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:03.908303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:07.658283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:11.574902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:29.854037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:34.218853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:39.019743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:41.935623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:45.228597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:48.781747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:52.076666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:57.670050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:01.056607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:04.315055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:08.232391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:11.812577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:30.120030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:34.636860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:39.337743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:42.233646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:45.507218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:49.113396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:52.278666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:58.001691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:01.275615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:04.694061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:08.446388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:12.143947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:30.502037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:35.184278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:39.550959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:42.436873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:45.701680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:49.421407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:52.503966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:02:58.220400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:01.485618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:05.066049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-23T16:03:08.652414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-23T16:03:23.300745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
AñoMbps (Media de bajada)HASTA 512 kbps+ 512 Kbps - 1 Mbps+ 1 Mbps - 6 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 Mbps+ 30 MbpsOTROSTotalIngresos (miles de pesos)TrimestreProvincia_xProvincia_yPeriodo
Año1.0000.8760.317-0.027-0.1370.1460.1530.3100.7110.7420.3020.9940.0940.0000.0000.999
Mbps (Media de bajada)0.8761.0000.280-0.029-0.1160.1270.1410.2830.6390.6810.2700.8810.0670.2460.0000.331
HASTA 512 kbps0.3170.2801.0000.0490.1020.1930.1250.1190.2460.2770.2300.3190.0700.0000.3550.201
+ 512 Kbps - 1 Mbps-0.027-0.0290.0491.0000.2030.1320.154-0.0390.0720.0230.188-0.0340.0680.0000.3120.215
+ 1 Mbps - 6 Mbps-0.137-0.1160.1020.2031.0000.6080.3630.1470.326-0.0270.763-0.1380.0620.0000.4250.179
+ 6 Mbps - 10 Mbps0.1460.1270.1930.1320.6081.0000.4850.3050.4900.1540.6870.1490.0860.0000.3590.186
+ 10 Mbps - 20 Mbps0.1530.1410.1250.1540.3630.4851.0000.2290.4440.2030.5490.1520.0780.0000.2640.205
+ 20 Mbps - 30 Mbps0.3100.2830.119-0.0390.1470.3050.2291.0000.3960.2700.3220.3110.0910.0000.2570.187
+ 30 Mbps0.7110.6390.2460.0720.3260.4900.4440.3961.0000.5690.7250.7160.0430.0000.3310.189
OTROS0.7420.6810.2770.023-0.0270.1540.2030.2700.5691.0000.3200.7440.0990.0000.2400.202
Total0.3020.2700.2300.1880.7630.6870.5490.3220.7250.3201.0000.3040.0480.0000.5970.157
Ingresos (miles de pesos)0.9940.8810.319-0.034-0.1380.1490.1520.3110.7160.7440.3041.0000.3510.0000.0000.999
Trimestre0.0940.0670.0700.0680.0620.0860.0780.0910.0430.0990.0480.3511.0000.0000.0000.999
Provincia_x0.0000.2460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
Provincia_y0.0000.0000.3550.3120.4250.3590.2640.2570.3310.2400.5970.0000.0000.0001.0000.000
Periodo0.9990.3310.2010.2150.1790.1860.2050.1870.1890.2020.1570.9990.9990.0000.0001.000

Missing values

2023-04-23T16:03:12.713814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-23T16:03:13.463735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AñoTrimestreProvincia_xMbps (Media de bajada)Provincia_yHASTA 512 kbps+ 512 Kbps - 1 Mbps+ 1 Mbps - 6 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 Mbps+ 30 MbpsOTROSTotalIngresos (miles de pesos)Periodo
020223Buenos Aires70Buenos Aires29.98527.709290315.0297.915267.044124.1903618689.065.8214721668.067055930.0Jul-Sept 2022
120223Buenos Aires70Capital Federal517.0005.74234371.067.82951.94628.6921253105.0105.4771547679.067055930.0Jul-Sept 2022
220223Buenos Aires70Catamarca71.000384.0003107.05.3895.0993.73750298.02.20870293.067055930.0Jul-Sept 2022
320223Buenos Aires70Chaco461.000987.00016782.018.9388.04915.82879390.03.711144146.067055930.0Jul-Sept 2022
420223Buenos Aires70Chubut109.0001.44445707.030.94034.68215.30917563.020.024165778.067055930.0Jul-Sept 2022
520223Buenos Aires70Córdoba99.00011.312153324.0111.61570.98927.112650344.013.8731038668.067055930.0Jul-Sept 2022
620223Buenos Aires70Corrientes67.0003.86523427.023.9487.77621.70656950.07.107144846.067055930.0Jul-Sept 2022
720223Buenos Aires70Entre Ríos107.0005.54947210.046.85518.26332.021102195.016.759268959.067055930.0Jul-Sept 2022
820223Buenos Aires70Formosa97.000307.00023538.019.5456.194564.00017704.0589.00068538.067055930.0Jul-Sept 2022
920223Buenos Aires70Jujuy58.0001.87919135.015.25436.083519.00045895.00.000118823.067055930.0Jul-Sept 2022
AñoTrimestreProvincia_xMbps (Media de bajada)Provincia_yHASTA 512 kbps+ 512 Kbps - 1 Mbps+ 1 Mbps - 6 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 Mbps+ 30 MbpsOTROSTotalIngresos (miles de pesos)Periodo
2015020141Tucumán3Neuquén4.133987.00077148.084.0001.5822.022.00.083958.02984054.0Ene-Mar 2014
2015120141Tucumán3Río Negro4.6704.61884304.073.0001.0621.08.00.094736.02984054.0Ene-Mar 2014
2015220141Tucumán3Salta53.00019.67764061.07.192314.0000.00.00.091297.02984054.0Ene-Mar 2014
2015320141Tucumán3San Juan531.0002.00051056.00.0000.0000.00.00.051589.02984054.0Ene-Mar 2014
2015420141Tucumán3San Luis7.0003.00012544.00.0001.0000.02.00.012557.02984054.0Ene-Mar 2014
2015520141Tucumán3Santa Cruz161.0001.62524972.01.0001.0000.00.00.026760.02984054.0Ene-Mar 2014
2015620141Tucumán3Santa Fe8.456124.468345225.020.3286.84523.0668.00.0506013.02984054.0Ene-Mar 2014
2015720141Tucumán3Santiago Del Estero1.23410.53122817.02.422109.0000.00.00.037113.02984054.0Ene-Mar 2014
2015820141Tucumán3Tierra Del Fuego12.000607.00030902.06.0000.0000.00.00.031527.02984054.0Ene-Mar 2014
2015920141Tucumán3Tucumán6.00034.67283210.011.779362.0003.00.00.0130032.02984054.0Ene-Mar 2014